Auswahl der wissenschaftlichen Literatur zum Thema „Deep generative modeling“
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Zeitschriftenartikel zum Thema "Deep generative modeling":
Blaschke, Thomas, und Jürgen Bajorath. „Compound dataset and custom code for deep generative multi-target compound design“. Future Science OA 7, Nr. 6 (Juli 2021): FSO715. http://dx.doi.org/10.2144/fsoa-2021-0033.
Joshi, Ameya, Minsu Cho, Viraj Shah, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian und Chinmay Hegde. „InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 4377–84. http://dx.doi.org/10.1609/aaai.v34i04.5863.
Lai, Peter, und Feruza Amirkulova. „Acoustic metamaterial design using Conditional Wasserstein Generative Adversarial Networks“. Journal of the Acoustical Society of America 151, Nr. 4 (April 2022): A253. http://dx.doi.org/10.1121/10.0011234.
Strokach, Alexey, und Philip M. Kim. „Deep generative modeling for protein design“. Current Opinion in Structural Biology 72 (Februar 2022): 226–36. http://dx.doi.org/10.1016/j.sbi.2021.11.008.
Lopez, Romain, Jeffrey Regier, Michael B. Cole, Michael I. Jordan und Nir Yosef. „Deep generative modeling for single-cell transcriptomics“. Nature Methods 15, Nr. 12 (30.11.2018): 1053–58. http://dx.doi.org/10.1038/s41592-018-0229-2.
Lee, Ung-Gi, Sang-Hee Kang, Jong-Chan Lee, Seo-Yeon Choi, Ukmyung Choi und Cheol-Il Lim. „Development of Deep Learning-based Art Learning Support Tool: Using Generative Modeling“. Korean Association for Educational Information and Media 26, Nr. 1 (31.03.2020): 207–36. http://dx.doi.org/10.15833/kafeiam.26.1.207.
Behnia, Farnaz, Dominik Karbowski und Vadim Sokolov. „Deep generative models for vehicle speed trajectories“. Applied Stochastic Models in Business and Industry 39, Nr. 5 (September 2023): 701–19. http://dx.doi.org/10.1002/asmb.2816.
Janson, Giacomo, und Michael Feig. „Transferable deep generative modeling of intrinsically disordered protein conformations“. PLOS Computational Biology 20, Nr. 5 (23.05.2024): e1012144. http://dx.doi.org/10.1371/journal.pcbi.1012144.
Zhang, Chun, Liangxu Xie, Xiaohua Lu, Rongzhi Mao, Lei Xu und Xiaojun Xu. „Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery“. Molecules 29, Nr. 7 (27.03.2024): 1499. http://dx.doi.org/10.3390/molecules29071499.
Guliev, R. „Generative adversarial networks for modeling reservoirs with permeability anisotropy“. IOP Conference Series: Materials Science and Engineering 1201, Nr. 1 (01.11.2021): 012066. http://dx.doi.org/10.1088/1757-899x/1201/1/012066.
Dissertationen zum Thema "Deep generative modeling":
Skalic, Miha 1990. „Deep learning for drug design : modeling molecular shapes“. Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/667503.
El disseny de drogues novells es un procés complex que requereix trobar les molècules adequades, entre un gran ventall de possibilitats, que siguin capaces d’unir-se a la proteïna desitjada amb unes propietats fisicoquímiques favorables. Els mètodes d’aprenentatge automàtic ens serveixen per a aprofitar dades antigues sobre les molècules i utilitzar-les per a noves prediccions, ajudant en el procés de selecció de molècules potencials sense la necessitat exclusiva d’experiments. Particularment, l’aprenentatge profund pot sera plicat per a extreure patrons complexos a partir de representacions simples. En aquesta tesi utilitzem l’aprenentatge profund per a extreure patrons a partir de representacions tridimensionals de molècules. Apliquem models de classificació i regressió per a predir la bioactivitat i l’afinitat d’unió, respectivament. A més, demostrem que podem predir les propietats dels lligands per a una cavitat proteica determinada. Finalment, utilitzem un model generatiu profund per a disseny de compostos. Donada una forma d’un lligand demostrem que podem generar compostos similars i, donada una cavitat proteica, podem generar compostos que potencialment s’hi podràn unir.
Chen, Tian Qi. „Deep kernel mean embeddings for generative modeling and feedforward style transfer“. Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62668.
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Brodie, Michael B. „Methods for Generative Adversarial Output Enhancement“. BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8763.
Testolin, Alberto. „Modeling cognition with generative neural networks: The case of orthographic processing“. Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424619.
In questa tesi vengono studiati alcuni processi cognitivi utilizzando recenti modelli di reti neurali generative. A differenza della maggior parte dei modelli connessionisti, l’approccio computazionale adottato in questa tesi enfatizza la natura generativa della cognizione, suggerendo che uno degli obiettivi principali dei sistemi cognitivi sia quello di apprendere un modello interno dell’ambiente circostante, che può essere usato per inferire relazioni causali ed effettuare previsioni riguardo all’informazione sensoriale in arrivo. In particolare, viene considerata una potente classe di reti neurali ricorrenti in grado di apprendere modelli generativi probabilistici dall’esperienza, estraendo informazione statistica di ordine superiore da un insieme di variabili in modo totalmente non supervisionato. Questo tipo di reti può essere formalizzato utilizzando la teoria dei modelli grafici probabilistici, che consente di descrivere con lo stesso linguaggio formale sia modelli di reti neurali che modelli Bayesiani strutturati. Inoltre, architetture di rete di base possono essere estese per creare sistemi più sofisticati, sfruttando molteplici livelli di processamento per apprendere modelli generativi gerarchici o sfruttando connessioni ricorrenti direzionate per processare informazione organizzata in sequenze. Riteniamo che queste architetture avanzate costituiscano un’alternativa promettente alle più tradizionali reti neurali supervisionate di tipo feed-forward, perché riproducono più fedelmente l’organizzazione funzionale e strutturale dei circuiti corticali, consentendo di spiegare come l’evidenza sensoriale possa essere effettivamente combinata con informazione contestuale proveniente da connessioni di feedback (“top-down”). Per giustificare l’utilizzo di questo tipo di modelli, in una serie di simulazioni studiamo nel dettaglio come implementazioni efficienti di reti generative gerarchiche e temporali possano estrarre informazione da grandi basi di dati, contenenti migliaia di esempi di training. In particolare, forniamo evidenza empirica relativa al riconoscimento di caratteri stampati e manoscritti appartenenti a diversi sistemi di scrittura, che possono in seguito essere combinati spazialmente o temporalmente per costruire unità ortografiche più complesse come quelle rappresentate dalle parole inglesi.
Yan, Guowei. „Interactive Modeling of Elastic Materials and Splashing Liquids“. The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1593098802306904.
Sadok, Samir. „Audiovisual speech representation learning applied to emotion recognition“. Electronic Thesis or Diss., CentraleSupélec, 2024. http://www.theses.fr/2024CSUP0003.
Emotions are vital in our daily lives, becoming a primary focus of ongoing research. Automatic emotion recognition has gained considerable attention owing to its wide-ranging applications across sectors such as healthcare, education, entertainment, and marketing. This advancement in emotion recognition is pivotal for fostering the development of human-centric artificial intelligence. Supervised emotion recognition systems have significantly improved over traditional machine learning approaches. However, this progress encounters limitations due to the complexity and ambiguous nature of emotions. Acquiring extensive emotionally labeled datasets is costly, time-intensive, and often impractical.Moreover, the subjective nature of emotions results in biased datasets, impacting the learning models' applicability in real-world scenarios. Motivated by how humans learn and conceptualize complex representations from an early age with minimal supervision, this approach demonstrates the effectiveness of leveraging prior experience to adapt to new situations. Unsupervised or self-supervised learning models draw inspiration from this paradigm. Initially, they aim to establish a general representation learning from unlabeled data, akin to the foundational prior experience in human learning. These representations should adhere to criteria like invariance, interpretability, and effectiveness. Subsequently, these learned representations are applied to downstream tasks with limited labeled data, such as emotion recognition. This mirrors the assimilation of new situations in human learning. In this thesis, we aim to propose unsupervised and self-supervised representation learning methods designed explicitly for multimodal and sequential data and to explore their potential advantages in the context of emotion recognition tasks. The main contributions of this thesis encompass:1. Developing generative models via unsupervised or self-supervised learning for audiovisual speech representation learning, incorporating joint temporal and multimodal (audiovisual) modeling.2. Structuring the latent space to enable disentangled representations, enhancing interpretability by controlling human-interpretable latent factors.3. Validating the effectiveness of our approaches through both qualitative and quantitative analyses, in particular on emotion recognition task. Our methods facilitate signal analysis, transformation, and generation
Luc, Pauline. „Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos“. Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM024/document.
Predictive models of the environment hold promise for allowing the transfer of recent reinforcement learning successes to many real-world contexts, by decreasing the number of interactions needed with the real world.Video prediction has been studied in recent years as a particular case of such predictive models, with broad applications in robotics and navigation systems.While RGB frames are easy to acquire and hold a lot of information, they are extremely challenging to predict, and cannot be directly interpreted by downstream applications.Here we introduce the novel tasks of predicting semantic and instance segmentation of future frames.The abstract feature spaces we consider are better suited for recursive prediction and allow us to develop models which convincingly predict segmentations up to half a second into the future.Predictions are more easily interpretable by downstream algorithms and remain rich, spatially detailed and easy to obtain, relying on state-of-the-art segmentation methods.We first focus on the task of semantic segmentation, for which we propose a discriminative approach based on adversarial training.Then, we introduce the novel task of predicting future semantic segmentation, and develop an autoregressive convolutional neural network to address it.Finally, we extend our method to the more challenging problem of predicting future instance segmentation, which additionally segments out individual objects.To deal with a varying number of output labels per image, we develop a predictive model in the space of high-level convolutional image features of the Mask R-CNN instance segmentation model.We are able to produce visually pleasing segmentations at a high resolution for complex scenes involving a large number of instances, and with convincing accuracy up to half a second ahead
Ionascu, Beatrice. „Modelling user interaction at scale with deep generative methods“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.
Förståelse för hur användare interagerar med ett företags tjänst är essentiell för data-drivna affärsverksamheter med ambitioner om att bättre tillgodose dess användare och att förbättra deras utbud. Generativ maskininlärning möjliggör modellering av användarbeteende och genererande av ny data i syfte att simulera eller identifiera och förklara typiska användarmönster. I detta arbete introducerar vi ett tillvägagångssätt för storskalig modellering av användarinteraktion i en klientservice-modell. Vi föreslår en ny representation av multivariat tidsseriedata i form av tidsbilder vilka representerar temporala korrelationer via spatial organisering. Denna representation delar två nyckelegenskaper som faltningsnätverk har utvecklats för att exploatera, vilket tillåter oss att utveckla ett tillvägagångssätt baserat på på djupa generativa modeller som bygger på faltningsnätverk. Genom att introducera detta tillvägagångssätt för tidsseriedata expanderar vi applicering av faltningsnätverk inom domänen för multivariat tidsserie, specifikt för användarinteraktionsdata. Vi använder ett tillvägagångssätt inspirerat av ramverket β-VAE i syfte att lära modellen gömda faktorer som definierar olika användarmönster. Vi utforskar olika värden för regulariseringsparametern β och visar att det är möjligt att konstruera en modell som lär sig en latent representation av identifierbara och multipla användarbeteenden. Vi visar med verklig data att modellen genererar realistiska exempel vilka i sin tur fångar statistiken på populationsnivå hos användarinteraktionsdatan, samt lär olika användarbeteenden och bidrar med precisa imputationer av saknad data.
McClintick, Kyle W. „Training Data Generation Framework For Machine-Learning Based Classifiers“. Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1276.
Fang, Zhufeng. „USING GEOSTATISTICS, PEDOTRANSFER FUNCTIONS TO GENERATE 3D SOIL AND HYDRAULIC PROPERTY DISTRIBUTIONS FOR DEEP VADOSE ZONE FLOW SIMULATIONS“. Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/193439.
Bücher zum Thema "Deep generative modeling":
Tomczak, Jakub M. Deep Generative Modeling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93158-2.
Ganem, Gabriel Loaiza. Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience. [New York, N.Y.?]: [publisher not identified], 2019.
Yahi, Alexandre. Simulating drug responses in laboratory test time series with deep generative modeling. [New York, N.Y.?]: [publisher not identified], 2019.
Tomczak, Jakub. Deep Generative Modeling. Springer International Publishing AG, 2022.
Hartnett, Gavin, Raffaele Vardavas, Lawrence Baker, Michael Chaykowsky, C. Ben Gibson, Federico Girosi, David Kennedy und Osonde Osoba. Deep Generative Modeling in Network Science with Applications to Public Policy Research. RAND Corporation, 2020. http://dx.doi.org/10.7249/wra843-1.
Bongard, Josh. Modeling self and others. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0011.
Buchteile zum Thema "Deep generative modeling":
Tomczak, Jakub M. „Hybrid Modeling“. In Deep Generative Modeling, 129–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_5.
Tomczak, Jakub M. „Generative Adversarial Networks“. In Deep Generative Modeling, 159–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_7.
Tomczak, Jakub M. „Deep Generative Modeling for Neural Compression“. In Deep Generative Modeling, 173–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_8.
Tomczak, Jakub M. „Autoregressive Models“. In Deep Generative Modeling, 13–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_2.
Tomczak, Jakub M. „Energy-Based Models“. In Deep Generative Modeling, 143–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_6.
Tomczak, Jakub M. „Flow-Based Models“. In Deep Generative Modeling, 27–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_3.
Tomczak, Jakub M. „Why Deep Generative Modeling?“ In Deep Generative Modeling, 1–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_1.
Tomczak, Jakub M. „Latent Variable Models“. In Deep Generative Modeling, 57–127. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_4.
Paluszek, Michael, Stephanie Thomas und Eric Ham. „Generative Modeling of Music“. In Practical MATLAB Deep Learning, 269–88. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7912-0_14.
Gu, Xi, Yuanyuan Xu und Kun Zhu. „Semantic Importance-Based Deep Image Compression Using a Generative Approach“. In MultiMedia Modeling, 70–81. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53308-2_6.
Konferenzberichte zum Thema "Deep generative modeling":
Caccia, Lucas, Herke van Hoof, Aaron Courville und Joelle Pineau. „Deep Generative Modeling of LiDAR Data“. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968535.
Davoody, Amirhossein, Ananda S. Roy und Sivakumar P. Mudanai. „Deep Generative Model for Device Variation Modeling“. In 2023 International Electron Devices Meeting (IEDM). IEEE, 2023. http://dx.doi.org/10.1109/iedm45741.2023.10413830.
Bianco, Michael J., Sharon Gannot und Peter Gerstoft. „Semi-Supervised Source Localization with Deep Generative Modeling“. In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231825.
Li, Zhaoyu, Son P. Nguyen, Dong Xu und Yi Shang. „Protein Loop Modeling Using Deep Generative Adversarial Network“. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. http://dx.doi.org/10.1109/ictai.2017.00166.
Fatir Ansari, Abdul, Jonathan Scarlett und Harold Soh. „A Characteristic Function Approach to Deep Implicit Generative Modeling“. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00750.
Liu, Yiding, Kaiqi Zhao, Gao Cong und Zhifeng Bao. „Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling“. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00087.
Ghimire, Sandesh, und Linwei Wang. „Deep Generative Modeling and Analysis of Cardiac Transmembrane Potential“. In 2018 Computing in Cardiology Conference. Computing in Cardiology, 2018. http://dx.doi.org/10.22489/cinc.2018.075.
Dai, Mengyu, und Haibin Hang. „Manifold Matching via Deep Metric Learning for Generative Modeling“. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00652.
Harris, Mark Wesley, und Sudhanshu Semwal. „Deep Rendering Graphics Pipeline“. In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3002.11.
Harris, Mark Wesley, und Sudhanshu Semwal. „Deep Rendering Graphics Pipeline“. In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3101.11.
Berichte der Organisationen zum Thema "Deep generative modeling":
Sadoune, Igor, Marcelin Joanis und Andrea Lodi. Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data. CIRANO, September 2023. http://dx.doi.org/10.54932/lqog8430.
Huang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen und Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), Februar 2024. http://dx.doi.org/10.21079/11681/48221.
Skyllingstad, Eric D. Next Generation Modeling for Deep Water Wave Breaking and Langmuir Circulation. Fort Belvoir, VA: Defense Technical Information Center, April 2009. http://dx.doi.org/10.21236/ada498290.
Skyllingstad, Eric D. Next Generation Modeling for Deep Water Wave Breaking and Langmuir Circulation. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada534062.
Beaulieu, Stace E., Karen Stocks und Leslie M. Smith. FAIR Data Training for Deep Ocean Early Career Researchers: Syllabus and slide presentations. Woods Hole Oceanographic Institution, Februar 2024. http://dx.doi.org/10.1575/1912/67631.
Buesseler, Buessele, Daniele Bianchi, Fei Chai, Jay T. Cullen, Margaret Estapa, Nicholas Hawco, Seth John et al. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, Oktober 2023. http://dx.doi.org/10.1575/1912/67120.